Impact of noise to edge detection on images of different complexity

Abstract

In this paper, an analysis of the edge detection over images of different complexity affected by Salt and Pepper, Gaussian and Speckle noise is performed. An analysis was performed for three noise levels, 0.01, 0.05 and 0.1. Over 100 images from the BSD database were used for analysis and each image has a GroundTruth with which an objective assessment of the detected edges was performed using PR and F measures. Five edge detectors Canny, LoG, Sobel, Prewitt and Roberts operator were used. The results are presented graphically. The obtained results show that noise significantly affects the detection of edges. When it comes to Salt and Pepper noise, Canny detector has achieved the best results for all levels of noise and image complexity. With the Speckle noise type for high and medium number of details in the image, Canny also gave the best results, while for low number of details in the image it is the Prewitt operator. When it comes to Gaussian noise, for all three categories of image complexity the best operator is Prewitt.